To perform a fair comparison between the “naive" and the “model-informed" strategy, \(N\)  and \(\tilde{N}\)  have the same number of hidden layers, units per layer, and activation functions. Training is performed the same way: the database is split in two for the validation (20% of samples) and training (80% of samples), and an Adam optimizer [1]} is used with the same learning rate to minimize the mean square error between the network output and the actual value of \(f\) . No extra penalization or more advanced technique is used here. Training is performed until an early-stopping criteria (based on the validation loss) is met. Full details are given in .
